Retentioneering 是一个开源的 Python 库，用于通过事件日志理解用户行为。找出用户卡住的地方，比较不同群体的路径，发现点击、会话和重复访问的模式。你可以自己探索数据，或者让 AI 代理使用同样的工具。

在 Google Colab 中运行演示笔记本——无需本地安装库，包含样本数据。

快速入门 ·使用案例 ·使用AI代理 ·文档

过渡图控件交互功能演示。你可以高亮路线，比较路径组，专注于你想调查的步骤，并在自我视图中探索进出过渡概率。

是给你的吗？

当你对行动顺序有疑问时，可以使用Retentioneering，比如这些：

• 一个指标变了。出行在哪里改变了，指标又在哪里变了？
• 用户在达到目标前就停下来了。他们会做什么？成功的路径有何不同？
• 你想了解参与度。代表了哪些行为类型，使用如何随着访问次数变化？
• 你需要调查一个流动。

Retentioneering

Retentioneering is an open-source Python library for understanding user behavior from event logs. Find where users get stuck, compare the paths different groups take, and discover patterns across clicks, sessions and repeat visits. Explore the data yourself or let an AI agent work with the same tools.

Run a demo notebook in Google Colab – no need to install the library locally, sample data included.

Quick start · Use cases · Use an AI agent · Docs

A demo of the interactive features of the transition graph widget. You can highlight a route, compare path groups, focus on the step you want to investigate, and explore incoming and outgoing transition probabilities in the ego view.

Is it for you?

Use Retentioneering when you have a question about the sequence of actions like these:

• A metric changed. Where did journeys change and move the metric?
• Users stop before reaching a goal. What do they do instead and how do successful paths differ?
• You want to understand engagement. What behavior types are represented, and how does usage evolve across visits?
• You need to investigate a flow. Where do checkout, onboarding, support or agent runs loop, branch or stall?